Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells59
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.8 KiB
Average record size in memory208.6 B

Variable types

Numeric16
Categorical10

Alerts

simbolizacion is highly overall correlated with perdidas-normalizadas and 3 other fieldsHigh correlation
perdidas-normalizadas is highly overall correlated with simbolizacion and 1 other fieldsHigh correlation
distancia-ejes is highly overall correlated with simbolizacion and 11 other fieldsHigh correlation
longitud is highly overall correlated with distancia-ejes and 9 other fieldsHigh correlation
ancho is highly overall correlated with distancia-ejes and 10 other fieldsHigh correlation
alto is highly overall correlated with simbolizacion and 3 other fieldsHigh correlation
peso-vacio is highly overall correlated with distancia-ejes and 8 other fieldsHigh correlation
motor-tamaño is highly overall correlated with distancia-ejes and 12 other fieldsHigh correlation
diametro is highly overall correlated with distancia-ejes and 9 other fieldsHigh correlation
carrera is highly overall correlated with marca and 1 other fieldsHigh correlation
radio-compresion is highly overall correlated with tipo-combustible and 3 other fieldsHigh correlation
caballos-fuerza is highly overall correlated with distancia-ejes and 11 other fieldsHigh correlation
peak-rpm is highly overall correlated with tipo-combustibleHigh correlation
ciudad-mpg is highly overall correlated with longitud and 7 other fieldsHigh correlation
carretera-mpg is highly overall correlated with distancia-ejes and 9 other fieldsHigh correlation
precio is highly overall correlated with distancia-ejes and 8 other fieldsHigh correlation
marca is highly overall correlated with distancia-ejes and 9 other fieldsHigh correlation
tipo-combustible is highly overall correlated with radio-compresion and 2 other fieldsHigh correlation
aspiracion is highly overall correlated with radio-compresion and 1 other fieldsHigh correlation
num-puertas is highly overall correlated with simbolizacion and 2 other fieldsHigh correlation
carroceria-estilo is highly overall correlated with num-puertasHigh correlation
ruedas-motrices is highly overall correlated with marcaHigh correlation
motor-ubicacion is highly overall correlated with perdidas-normalizadas and 5 other fieldsHigh correlation
motor-tipo is highly overall correlated with motor-tamaño and 3 other fieldsHigh correlation
num-cilindros is highly overall correlated with ancho and 6 other fieldsHigh correlation
sistema-combustible is highly overall correlated with radio-compresion and 3 other fieldsHigh correlation
tipo-combustible is highly imbalanced (53.9%)Imbalance
motor-ubicacion is highly imbalanced (89.0%)Imbalance
num-cilindros is highly imbalanced (57.6%)Imbalance
perdidas-normalizadas has 41 (20.0%) missing valuesMissing
diametro has 4 (2.0%) missing valuesMissing
carrera has 4 (2.0%) missing valuesMissing
precio has 4 (2.0%) missing valuesMissing
simbolizacion has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-10-04 23:23:19.599254
Analysis finished2023-10-04 23:24:35.840275
Duration1 minute and 16.24 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

simbolizacion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-10-04T23:24:35.980154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-10-04T23:24:36.216828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

perdidas-normalizadas
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)31.1%
Missing41
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:36.471318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile74
Q194
median115
Q3150
95-th percentile188
Maximum256
Range191
Interquartile range (IQR)56

Descriptive statistics

Standard deviation35.442168
Coefficient of variation (CV)0.29050957
Kurtosis0.52544039
Mean122
Median Absolute Deviation (MAD)24
Skewness0.76597642
Sum20008
Variance1256.1472
MonotonicityNot monotonic
2023-10-04T23:24:36.763773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161 11
 
5.4%
91 8
 
3.9%
150 7
 
3.4%
134 6
 
2.9%
128 6
 
2.9%
104 6
 
2.9%
85 5
 
2.4%
94 5
 
2.4%
65 5
 
2.4%
102 5
 
2.4%
Other values (41) 100
48.8%
(Missing) 41
20.0%
ValueCountFrequency (%)
65 5
2.4%
74 5
2.4%
77 1
 
0.5%
78 1
 
0.5%
81 2
 
1.0%
83 3
1.5%
85 5
2.4%
87 2
 
1.0%
89 2
 
1.0%
90 1
 
0.5%
ValueCountFrequency (%)
256 1
 
0.5%
231 1
 
0.5%
197 2
 
1.0%
194 2
 
1.0%
192 2
 
1.0%
188 2
 
1.0%
186 1
 
0.5%
168 5
2.4%
164 2
 
1.0%
161 11
5.4%

marca
Categorical

Distinct22
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
112 

Length

Max length13
Median length11
Mean length6.4780488
Min length3

Characters and Unicode

Total characters1328
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Length

2023-10-04T23:24:37.075773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Most occurring characters

ValueCountFrequency (%)
a 154
 
11.6%
o 152
 
11.4%
s 109
 
8.2%
t 100
 
7.5%
e 81
 
6.1%
u 76
 
5.7%
n 71
 
5.3%
i 68
 
5.1%
d 63
 
4.7%
m 57
 
4.3%
Other values (15) 397
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1317
99.2%
Dash Punctuation 11
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 154
 
11.7%
o 152
 
11.5%
s 109
 
8.3%
t 100
 
7.6%
e 81
 
6.2%
u 76
 
5.8%
n 71
 
5.4%
i 68
 
5.2%
d 63
 
4.8%
m 57
 
4.3%
Other values (14) 386
29.3%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1317
99.2%
Common 11
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 154
 
11.7%
o 152
 
11.5%
s 109
 
8.3%
t 100
 
7.6%
e 81
 
6.2%
u 76
 
5.8%
n 71
 
5.4%
i 68
 
5.2%
d 63
 
4.8%
m 57
 
4.3%
Other values (14) 386
29.3%
Common
ValueCountFrequency (%)
- 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 154
 
11.6%
o 152
 
11.4%
s 109
 
8.2%
t 100
 
7.5%
e 81
 
6.1%
u 76
 
5.7%
n 71
 
5.3%
i 68
 
5.1%
d 63
 
4.7%
m 57
 
4.3%
Other values (15) 397
29.9%

tipo-combustible
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2023-10-04T23:24:37.330003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:37.637326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiracion
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Length

2023-10-04T23:24:37.865968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:38.155519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 689
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 689
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

num-puertas
Categorical

Distinct2
Distinct (%)1.0%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
four
114 
two
89 

Length

Max length4
Median length4
Mean length3.5615764
Min length3

Characters and Unicode

Total characters723
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 114
55.6%
two 89
43.4%
(Missing) 2
 
1.0%

Length

2023-10-04T23:24:38.372002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:38.630402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
four 114
56.2%
two 89
43.8%

Most occurring characters

ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 723
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 723
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%
Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2023-10-04T23:24:38.856494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:39.162537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

ruedas-motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Length

2023-10-04T23:24:39.408267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:39.657647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 606
98.5%
Decimal Number 9
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 606
98.5%
Common 9
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

motor-ubicacion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Length

2023-10-04T23:24:39.873052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:40.149935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1022
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1022
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

distancia-ejes
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:40.389305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2023-10-04T23:24:40.687505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

longitud
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:40.985043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2023-10-04T23:24:41.311056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

ancho
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:41.617073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2023-10-04T23:24:41.916888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

alto
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:42.221663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2023-10-04T23:24:42.531652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

peso-vacio
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:42.816047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2023-10-04T23:24:43.110612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

motor-tipo
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-10-04T23:24:43.401115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:43.716885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

num-cilindros
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-10-04T23:24:44.744028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:45.258288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 800
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

motor-tamaño
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:45.739746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2023-10-04T23:24:46.257822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-10-04T23:24:46.671878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T23:24:47.161852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719
90.0%
Decimal Number 80
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 719
90.0%
Common 80
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

diametro
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)18.9%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.3297512
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:47.505983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.59
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.27353873
Coefficient of variation (CV)0.0821499
Kurtosis-0.8289454
Mean3.3297512
Median Absolute Deviation (MAD)0.26
Skewness0.02001551
Sum669.28
Variance0.074823438
MonotonicityNot monotonic
2023-10-04T23:24:47.783796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.78 8
 
3.9%
3.43 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 79
38.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

carrera
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)17.9%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.2554229
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:48.063996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31671745
Coefficient of variation (CV)0.097289189
Kurtosis2.0742435
Mean3.2554229
Median Absolute Deviation (MAD)0.17
Skewness-0.68312219
Sum654.34
Variance0.10030995
MonotonicityNot monotonic
2023-10-04T23:24:48.331289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (26) 83
40.5%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

radio-compresion
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:48.588237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-10-04T23:24:48.839041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

caballos-fuerza
Real number (ℝ)

Distinct59
Distinct (%)29.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean104.25616
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:49.111973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.4
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.714369
Coefficient of variation (CV)0.38093068
Kurtosis2.6232798
Mean104.25616
Median Absolute Deviation (MAD)25
Skewness1.3910295
Sum21164
Variance1577.2311
MonotonicityNot monotonic
2023-10-04T23:24:49.405017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
88 6
 
2.9%
62 6
 
2.9%
101 6
 
2.9%
160 6
 
2.9%
Other values (49) 115
56.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

peak-rpm
Real number (ℝ)

Distinct23
Distinct (%)11.3%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean5125.3695
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:49.663348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5990
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation479.33456
Coefficient of variation (CV)0.093521953
Kurtosis0.056526492
Mean5125.3695
Median Absolute Deviation (MAD)300
Skewness0.073236691
Sum1040450
Variance229761.62
MonotonicityNot monotonic
2023-10-04T23:24:49.896788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
5250 7
 
3.4%
4500 7
 
3.4%
5800 7
 
3.4%
4200 5
 
2.4%
Other values (13) 32
15.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

ciudad-mpg
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:50.140414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2023-10-04T23:24:50.393895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

carretera-mpg
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:50.678246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2023-10-04T23:24:50.924686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

precio
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct186
Distinct (%)92.5%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean13207.129
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-04T23:24:51.203808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.0663
Coefficient of variation (CV)0.60172549
Kurtosis3.2315369
Mean13207.129
Median Absolute Deviation (MAD)3306
Skewness1.8096753
Sum2654633
Variance63155863
MonotonicityNot monotonic
2023-10-04T23:24:51.497024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
8845 2
 
1.0%
8495 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
7957 2
 
1.0%
7775 2
 
1.0%
5572 2
 
1.0%
Other values (176) 181
88.3%
(Missing) 4
 
2.0%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2023-10-04T23:24:29.107662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:26.006463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:30.603561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:34.540487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:39.487509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:43.547204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:47.208310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:51.594742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:56.142493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:59.787321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:03.754828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:08.785302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:12.512116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:16.145444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:21.015836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:25.230114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:29.335661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:26.327095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:30.839385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:34.750007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:39.795768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:43.757009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:47.451363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:51.948704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:56.360965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:59.996083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:04.108249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:09.000790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:12.717217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:16.380654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:21.243876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:25.468248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:29.574180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:26.842290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:31.074226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:34.986483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:40.029710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:43.981830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:47.666533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:52.339127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:56.594491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:00.252386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:04.422889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:09.257013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:12.961850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:16.617149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:21.477595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:25.733292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:29.829658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:27.074244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:31.306637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:35.212152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:40.288679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:44.225619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:47.902600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:52.738486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:56.833723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:00.483440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:24:30.588524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:28.073441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:31.982926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:35.926834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:40.986096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:44.907038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:48.600086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:53.903237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:24:01.194828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:05.852554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:24:17.910297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:22.433465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:26.709946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:23:48.808799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:54.109175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:24:01.428753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:24:10.430017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:14.116760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:18.213287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:22.673884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:26.935642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:31.304467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:28.801205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:32.455180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:36.545797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:23:57.936924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:01.653551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:06.539539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:10.666213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:14.348782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-04T23:24:22.890347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:27.161789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:31.690202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:29.032165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:32.702208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:36.871806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:41.683712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:45.601593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:49.251780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:54.572715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:58.169358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:01.906533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:06.871477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:10.891985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:14.587722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:18.914668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:23.129341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:27.423612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:32.062377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:29.234991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:32.930112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:37.203720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:41.887425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:45.809134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:49.480787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:54.775629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:58.395819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:02.116641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:07.090834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:11.139660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:14.785228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:19.185498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:23.348600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:27.653416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:32.461943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:29.464979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:33.369230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:37.603214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:42.151353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:46.042164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:49.717370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:55.002840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:58.628231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:02.375786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:07.325067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:11.367327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:15.014137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:19.554593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:23.600519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:27.906235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:32.827021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:29.675505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:33.603436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:37.958074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:42.610416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:46.290120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:50.072900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:55.210379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:58.853623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:02.601549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:07.891282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:11.580600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:15.240604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:19.909910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:23.827806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:28.137029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:33.190305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:29.887638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:33.831069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:38.323102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:42.818271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:46.487152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:50.427713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:55.430439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:59.052763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:02.832987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:08.087580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:11.786155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:15.452642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:20.219105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:24.036324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:28.364963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:33.546525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:30.111102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:34.066664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:38.706100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:43.049797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:46.728748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:50.792252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:55.673720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:59.300458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:03.080522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:08.321958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:12.017573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:15.679043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:20.551002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:24.743368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:28.598229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:33.960444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:30.369387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:34.295928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:39.109389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:43.302904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:46.967370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:51.197151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:55.902690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:23:59.536603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:03.388140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:08.559967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:12.267438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:15.920906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:20.784491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:24.982164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T23:24:28.856668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-04T23:24:51.806672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
simbolizacionperdidas-normalizadasdistancia-ejeslongitudanchoaltopeso-vaciomotor-tamañodiametrocarreraradio-compresioncaballos-fuerzapeak-rpmciudad-mpgcarretera-mpgpreciomarcatipo-combustibleaspiracionnum-puertascarroceria-estiloruedas-motricesmotor-ubicacionmotor-tiponum-cilindrossistema-combustible
simbolizacion1.0000.527-0.538-0.396-0.254-0.523-0.256-0.177-0.182-0.0150.023-0.0100.285-0.0180.053-0.1430.4430.2170.1850.6820.3340.2660.2720.2220.1600.266
perdidas-normalizadas0.5271.000-0.1080.0210.114-0.3920.0860.081-0.0640.093-0.0510.2390.298-0.253-0.1970.1880.4400.1630.0000.4170.2050.3431.0000.3490.2760.151
distancia-ejes-0.538-0.1081.0000.9120.8120.6330.7650.6480.5410.224-0.1260.503-0.315-0.493-0.5390.6820.5070.3410.3100.4440.3340.4170.5680.3530.3160.226
longitud-0.3960.0210.9121.0000.8880.5250.8900.7830.6420.181-0.1930.663-0.272-0.670-0.6980.8100.5000.1100.2070.3610.2410.4090.0000.3170.3560.326
ancho-0.2540.1140.8120.8881.0000.3500.8640.7710.6110.241-0.1460.692-0.201-0.688-0.7010.8120.5270.2330.3010.2970.1280.4030.1600.3690.5670.246
alto-0.523-0.3920.6330.5250.3501.0000.3460.2000.226-0.0260.0000.009-0.301-0.069-0.1330.2640.4800.2770.2370.5360.4970.3600.2720.3880.3500.292
peso-vacio-0.2560.0860.7650.8900.8640.3461.0000.8780.7000.163-0.2190.807-0.238-0.813-0.8340.9140.4940.3050.3750.2670.2300.4560.1000.3270.4820.292
motor-tamaño-0.1770.0810.6480.7830.7710.2000.8781.0000.7250.293-0.2350.820-0.275-0.730-0.7210.8280.5330.1570.2710.2020.2020.4690.6190.5270.6420.333
diametro-0.182-0.0640.5410.6420.6110.2260.7000.7251.000-0.082-0.1650.647-0.310-0.620-0.6270.6490.5370.1700.3280.2150.1510.4390.3250.4350.2450.356
carrera-0.0150.0930.2240.1810.241-0.0260.1630.293-0.0821.000-0.0680.138-0.071-0.035-0.0340.1180.5800.3710.2590.1490.1520.3340.6140.4400.2560.328
radio-compresion0.023-0.051-0.126-0.193-0.1460.000-0.219-0.235-0.165-0.0681.000-0.355-0.0260.4790.445-0.1780.4930.9930.5540.1820.0480.1140.0000.3380.5210.518
caballos-fuerza-0.0100.2390.5030.6630.6920.0090.8070.8200.6470.138-0.3551.0000.112-0.913-0.8840.8510.4590.2200.3420.1780.1850.4010.8430.5150.5640.320
peak-rpm0.2850.298-0.315-0.272-0.201-0.301-0.238-0.275-0.310-0.071-0.0260.1121.000-0.132-0.056-0.0830.4690.5930.3140.2450.0640.2450.4470.3590.2830.364
ciudad-mpg-0.018-0.253-0.493-0.670-0.688-0.069-0.813-0.730-0.620-0.0350.479-0.913-0.1321.0000.968-0.8310.3600.3890.1860.0510.0000.3800.1100.2090.4240.304
carretera-mpg0.053-0.197-0.539-0.698-0.701-0.133-0.834-0.721-0.627-0.0340.445-0.884-0.0560.9681.000-0.8270.4040.3360.3190.1310.0000.4370.1010.3250.5000.341
precio-0.1430.1880.6820.8100.8120.2640.9140.8280.6490.118-0.1780.851-0.083-0.831-0.8271.0000.3700.3400.3940.0000.2400.4430.4680.2610.4470.287
marca0.4430.4400.5070.5000.5270.4800.4940.5330.5370.5800.4930.4590.4690.3600.4040.3701.0000.3700.4100.3000.3170.6030.7030.6290.5440.510
tipo-combustible0.2170.1630.3410.1100.2330.2770.3050.1570.1700.3710.9930.2200.5930.3890.3360.3400.3701.0000.3740.1490.1730.0880.0000.2500.1550.985
aspiracion0.1850.0000.3100.2070.3010.2370.3750.2710.3280.2590.5540.3420.3140.1860.3190.3940.4100.3741.0000.0000.0000.1180.0000.1500.1960.610
num-puertas0.6820.4170.4440.3610.2970.5360.2670.2020.2150.1490.1820.1780.2450.0510.1310.0000.3000.1490.0001.0000.7480.0510.0680.2010.1350.240
carroceria-estilo0.3340.2050.3340.2410.1280.4970.2300.2020.1510.1520.0480.1850.0640.0000.0000.2400.3170.1730.0000.7481.0000.2140.4380.1320.0680.144
ruedas-motrices0.2660.3430.4170.4090.4030.3600.4560.4690.4390.3340.1140.4010.2450.3800.4370.4430.6030.0880.1180.0510.2141.0000.1240.4250.3360.387
motor-ubicacion0.2721.0000.5680.0000.1600.2720.1000.6190.3250.6140.0000.8430.4470.1100.1010.4680.7030.0000.0000.0680.4380.1241.0000.3990.2880.000
motor-tipo0.2220.3490.3530.3170.3690.3880.3270.5270.4350.4400.3380.5150.3590.2090.3250.2610.6290.2500.1500.2010.1320.4250.3991.0000.5460.377
num-cilindros0.1600.2760.3160.3560.5670.3500.4820.6420.2450.2560.5210.5640.2830.4240.5000.4470.5440.1550.1960.1350.0680.3360.2880.5461.0000.373
sistema-combustible0.2660.1510.2260.3260.2460.2920.2920.3330.3560.3280.5180.3200.3640.3040.3410.2870.5100.9850.6100.2400.1440.3870.0000.3770.3731.000

Missing values

2023-10-04T23:24:34.386914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-04T23:24:35.150997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-04T23:24:35.623948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

simbolizacionperdidas-normalizadasmarcatipo-combustibleaspiracionnum-puertascarroceria-estiloruedas-motricesmotor-ubicaciondistancia-ejeslongitudanchoaltopeso-vaciomotor-tiponum-cilindrosmotor-tamañosistema-combustiblediametrocarreraradio-compresioncaballos-fuerzapeak-rpmciudad-mpgcarretera-mpgprecio
03NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212713495.0
13NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212716500.0
21NaNalfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500.0
32164.0audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950.0
42164.0audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.0115.05500.0182217450.0
52NaNaudigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250.0
61158.0audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.5110.05500.0192517710.0
71NaNaudigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.5110.05500.0192518920.0
81158.0audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.3140.05500.0172023875.0
90NaNaudigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.0160.05500.01622NaN
simbolizacionperdidas-normalizadasmarcatipo-combustibleaspiracionnum-puertascarroceria-estiloruedas-motricesmotor-ubicaciondistancia-ejeslongitudanchoaltopeso-vaciomotor-tiponum-cilindrosmotor-tamañosistema-combustiblediametrocarreraradio-compresioncaballos-fuerzapeak-rpmciudad-mpgcarretera-mpgprecio
195-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.5114.05400.0232813415.0
196-2103.0volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.5114.05400.0242815985.0
197-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.5114.05400.0242816515.0
198-2103.0volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.5162.05100.0172218420.0
199-174.0volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.5162.05100.0172218950.0
200-195.0volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.5114.05400.0232816845.0
201-195.0volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.7160.05300.0192519045.0
202-195.0volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485.0
203-195.0volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.0106.04800.0262722470.0
204-195.0volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.5114.05400.0192522625.0